A High-Precision Monitoring Method for Surface Subsidence in Western Chinese Mining Areas by Fusing InSAR and LiDAR

Surface subsidence monitoring in western Chinese mining areas is challenged by complex topography, dense vegetation, and large deformation gradients. Traditional single remote sensing methods suffer from inherent drawbacks: InSAR performs well in small-gradient areas but is prone to incoherence in large-gradient zones, whereas LiDAR achieves high accuracy in large-gradient monitoring but lacks precision for small-gradient deformations. This study proposes a high-precision monitoring method by fusing InSAR and LiDAR data. First, a non-growing surface feature extraction approach using LiDAR echo characteristics is adopted to reduce vegetation noise and improve monitoring stability. Second, fusion boundaries are determined using InSAR corrected deformation gradient and image coherence, dividing the subsidence basin into small-, medium-, and large-gradient zones. Third, an inverse mean squared error weighted fusion strategy is applied in the medium-gradient zone to realize reliable data integration. Experiments conducted at the Sihe Coal Mine show that the full-gradient RMSE of InSAR is 415 mm, and that of LiDAR is 56 mm. The proposed fusion method reduces the full-gradient RMSE to 39 mm, which is 91% lower than that of InSAR and 30% lower than that of LiDAR. The method achieves centimeter-level accuracy across the entire subsidence basin while maintaining high precision in both large-gradient centers and small-gradient edges. It provides a stable and practical technical solution for full-gradient subsidence monitoring in western Chinese mining areas with complex terrain and dense vegetation.